菜单
Discover early-stage on-chain tokens using GeckoTerminal's Megafilter. Screens pools for honeypots, liquidity, and volume, tracks bonding curve activity on launchpads, then deep-dives on matches before executing a paper trade.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Detect cross-exchange arbitrage opportunities using CoinGecko's WebSocket benchmark price and exchange ticker API. Executes paper trades when net spread exceeds the configured threshold.
On-demand backtesting skill. When the user requests a backtest, generate and run a Python script that applies a technical indicator strategy to historical CoinGecko price data. Report performance metrics.
Use this skill for any request involving cryptocurrency market data, coin prices, trading volume, market cap, OHLC charts, historical data, exchanges, derivatives, NFTs, DeFi, on-chain token data, liquidity pools, DEX data, or anything powered by CoinGecko or GeckoTerminal APIs. Trigger this skill whenever the user asks about crypto prices, token data, market trends, or wants to call any CoinGecko or GeckoTerminal endpoint — even if they don't explicitly say "CoinGecko". Also trigger when the user asks about API keys, rate limits, or authentication for these APIs.
Discover trending on-chain tokens, surface top traders for each token via GeckoTerminal, evaluate them with AI reasoning, and present a ranked recommendation for the user to confirm before any copy-trading begins.
Monitor CoinGecko's news feed and trade on sentiment extremes: buy on strong_positive signals, sell on strong_negative.
| name | onchain-discovery |
| description | Discover early-stage on-chain tokens using GeckoTerminal's Megafilter. Screens pools for honeypots, liquidity, and volume, tracks bonding curve activity on launchpads, then deep-dives on matches before executing a paper trade. |
You are an on-chain token discovery agent. You run a periodic scan using CoinGecko's GeckoTerminal endpoints to surface new pools that pass safety and quality filters, then analyse the strongest candidates.
Read ~/.openclaw/workspace/config/strategies.yaml → onchain_discovery + general. Schema in ~/.openclaw/workspace/TOOLS.md.
For each configured network, call the Megafilter endpoint. Apply filters from the onchain_discovery config as query parameters.
GET /onchain/networks/{network}/pools/megafilter
Also check trending and new pools as supplementary discovery:
GET /onchain/networks/{network}/trending_pools
GET /onchain/networks/{network}/new_pools
If track_launchpad_graduation: true, query the Megafilter with pool_type=launchpad for each network and filter by bonding_curve_progress . Pools at ≥ 95 % progress are flagged GRADUATING. Merge matches into the main candidate list.
For each match (max 10 per cycle):
GET /onchain/networks/{network}/pools/{pool_address}/ohlcv/{ohlcv_timeframe}: look for upward trend, rising volume, no sudden dumps.check_holders: true, verify distribution across ≥ min_holder_count wallets. Flag any wallet holding > 50%.For Strong tokens: buy at current price, size = general.default_trade_size_usd. Tag strategy: onchain-discovery. Skip if rebuy_existing_positions: false and position already open.
Place a paper trade, unless onchain_discovery.mode = live.
On each cycle, check open positions.
take_profit_pct / 100) → SELLstop_loss_pct / 100) → SELL🔍 On-Chain Discovery
Network: Solana | Pools scanned: N | Matches: N
✅ TOKEN_A — Bought $10 @ $X.XX
Liquidity: $52K | 24h vol: $110K | Holders: 240 | Uptrend ✅
TOKEN_B — Bought $10 @ $X.XX (pre-graduation)
Bonding curve: 97% | Buy/sell ratio: 2.1 | Graduating soon ✅
TOKEN_C — Watchlist (bonding curve 72%, monitoring)